DAPI-Based Polyphosphate Estimation with Extraction Sufficiency Validation:A Method for Quantifying Polyphosphate from Microalgae Samples v1
Bibliographic record
Abstract
The utilization of DAPI-based fluorometric estimation for polyphosphate (polyP) analysis from microalgae has become increasingly prevalent in field samples since its publication by Martin P. et al. This technique involves evaluating the fluorescence of DAPI-stained samples in quartz cuvettes using a spectrofluorometer. To reduce the consumption of reagent, time, and labor while minimizing DAPI photobleaching, we have adapted this method to a 96-well black microtiter plate with a black film-covered lid. Additionally, the calculation method has been modified to account for matrix effects in microplates. Testing the number of treatment rounds necessary to extract all polyP is crucial. However, even when collecting samples from the same field location or cultivation condition, there can be high variability in treatment rounds among replicates, leading to significant background fluorescence and rendering the polyP from the sample undetectable. This challenge is especially prominent when measuring polyP from field samples. Limited sample availability and insufficient polyP extraction, combined with high background fluorescence, make the laborious measurement unpredictable and hinder accurate polyP measurement. This obstacle is a significant hurdle in polyP measurement. In our assay, we overcome the challenge by validating the sufficiency of extraction for each sample and then measuring the polyP values. To conduct the assay, roughly 400 uL RNase, 400 uL DNase, and 700 uL proteinase are required for four samples.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".